A single pass through the Agents Hub: open the builder, describe the agent in plain English, review the generated draft, and confirm. No code, no configuration files.
VDF AI Agents is more than just another AI tool—it's your complete AI operating system for enterprise value delivery.
VDF AI Agents includes the execution, tooling, knowledge, observability, and orchestration foundations needed for production use.
Connect any LLM or SLM as the agent's model. Mix OpenAI, Anthropic, Azure OpenAI, Mistral, Ollama, and compatible endpoints per agent.
Guide teams through Basics → Model → Tools & Skills → Prompt → Review before saving an agent.
Assign tools from an MCP-powered registry including web search, database query, file reader, calculators, and custom tools.
Connect databases, Slack, GitHub, APIs, and knowledge bases to any agent so every workflow starts from live business context.
Monitor executions, latency, success rate, cost, and energy per agent in real time from one operational dashboard.
Register agents for use in VDF AI Networks orchestration flows and promote them from standalone assistants to reusable network nodes.
Work smarter with agentic AI that understands your entire ecosystem.
Trigger a prompt, delegate a task, or let agents autonomously draft user stories, summarize meeting transcripts, search GitHub/Jira semantically, write documentation, and analyze velocity patterns.
Connect databases, Slack, GitHub, APIs, Jira, Confluence, GitBook, and Notion to any agent. Give each workflow the live knowledge sources it needs to read, reason, and act.
Dictate tasks, notes, or backlog items using Voice Dictation in any language. Perfect for hands-free work in meetings, during transit, or for accessibility.
Dynamic fallback agents for higher reliability, fine-tuned reasoning across organizational knowledge, configurable orchestration logic, and RAG-powered semantic memory.
No surprise overage charges, unlimited usage per seat, transparent and scalable model. Whether you're a startup team or enterprise program, your pricing is predictable.
OAuth-secured integrations for compliance-heavy orgs, audit logging, GDPR and EU data residency support, SSO & Role-based Access Control.
Looking for an open, flexible, and affordable alternative to Copilot? You found it.
| Feature | VDF AI Agents | Microsoft Copilot |
|---|---|---|
| Works with Jira, Slack, GitHub, GitBook, Notion | Yes | Limited or No |
| Multi-Agent Orchestration | Yes | No |
| Voice Dictation in All Languages | Yes | English-biased |
| Flat Per-Seat Pricing | Yes | Token-based & unclear |
| Manual + Agent Modes | Both | Agent design via Studio only |
| Semantic Search Across Tools | Yes | Yes (limited to Microsoft Graph) |
| On-Premise / Air-Gapped Deployment | Yes — full on-premise | No — Azure cloud only |
| Per-Agent Audit Log & Execution Trace | Yes — every prompt, tool call, output | Partial — Purview logs, no per-step trace |
| Tool-Level Least-Privilege Access | Yes — scoped per agent | Inherits tenant permissions |
| Human Approval Gates for High-Risk Actions | Yes — built into workflow | Limited — manual review only |
| Model Choice (OpenAI, Anthropic, Mistral, Ollama) | Any model or provider | Azure OpenAI only |
| EU AI Act High-Risk System Controls | Immutable logs, approval gates, human oversight | Requires additional tooling |
Every agent deployed on VDF AI follows a controlled lifecycle where identity, tools, behavior, and limits are defined before any execution begins.
Every VDF AI agent is defined under governance, run inside a controlled loop, and traced end to end — all on infrastructure you own.
A 5-step checklist makes the complete agent configuration visible before use.
Each task cycles through a bounded loop — nothing runs outside its limits.
Every run is captured as a replayable record for compliance and debugging.
The five-step Agent Builder moves through Basics → Model → Tools & Skills → Prompt → Review so identity, capabilities, and instructions remain inspectable before the agent is saved.
Learn about AI agent governance →Every execution is captured as an immutable audit log: input → retrieval hits → tool calls → model response → output. Reconstruct any decision for compliance review or incident investigation.
See on-premise deployment options →Register agents into VDF AI Networks for multi-agent orchestration. Route each task to the right model, enforce per-node tool permissions, and observe the whole network as one auditable execution graph.
Read about AI agent orchestration →All the features and integrations you need to get started.
From the engineers who build agents to the leaders who govern them, VDF AI Agents gives each team the depth it needs — without ever surrendering control of your models, data, or infrastructure.
Design and configure production agents through Basics, Model, Tools & Skills, Prompt, and Review—with no infrastructure glue.
OutcomeProduction-ready agents in days, not quarters
Ground agents in your own systems so every answer is accurate, current, and traceable back to source.
OutcomeTrustworthy answers grounded in your data
Put every agent action under policy, approval, and immutable audit — engineered for regulated environments from day one.
OutcomeEU AI Act-ready governance by default
Turn high-volume, repetitive workflows across Finance, HR, Support, and Procurement into governed, orchestrated agents.
OutcomeHours of manual work reclaimed each week
Accelerate the full delivery lifecycle with agents embedded directly in the tools your teams already use.
OutcomeFaster cycles with less coordination overhead
Scale AI across the organization with full control over cost, sovereignty, and measurable business outcomes.
OutcomeEnterprise AI you can scale with confidence
Not sure where your team fits? See VDF AI Agents mapped to real-world workflows.
Explore all use casesPre-built, governed agents across 20 categories — ready to deploy on your infrastructure.
The Agents Hub is the admin and management portal for VDF AI Agents. Configure each agent through Basics → Model → Tools & Skills → Prompt → Review without writing infrastructure code.
Define the agent’s identity, purpose, category, and visibility before choosing how it will run.
Choose the provider, model, and generation parameters that fit the agent’s workload and deployment policy.
Assign governed capabilities and reusable procedures. Missing tools declared by a Skill are reported for deliberate review.
Define the enduring role, response behaviour, knowledge context, escalation rules, and boundaries for the agent.
Inspect the complete agent, resolve tool and Skill gaps, and confirm the configuration before saving.
Package specialised instructions and supporting resources as validated, versioned Skills. Assign the procedure in Tools & Skills, then grant only the capabilities the agent is permitted to use.
Start with eight maintained system procedures or create private methods for work that is specific to your organisation.
Discover compact descriptions first, load full instructions when relevant, and read bundled references only when a step needs them.
A Skill can declare required tools but never grants them. Agent assignments, connector access, and approval policy remain separate controls.
Connect any LLM or SLM as the agent's model using OpenAI, Anthropic, Azure OpenAI, Mistral, Ollama, or any OpenAI-compatible endpoint.
Per-agent temperature, max_tokens, and top_p controls. Configuration is agent-scoped — changes to one agent never affect others.
Automatic fallback to secondary providers on rate-limit or error. Agents stay operational without manual intervention.
Pin agents to specific model versions for production stability. Test new versions in staging before rolling out to the organization.
Run two agents simultaneously against the same prompt. Compare response quality, latency, and behavior.
Full chat interface embedded within the admin portal. No context switching — test and refine in one place.
The Playground removes the guesswork from agent tuning. Teams compare outputs directly — without staging environments or separate tooling — reducing agent iteration cycles significantly.
Track executions, latency, success rate, cost, and energy per agent from a single operational view.
Every agent invocation logged with input, output, model used, latency, token count, success state, and final status.
Complete audit trail across all user sessions. Filter by agent, user, date range, or execution status.
P50/P95 latency, success rate, error rates, throughput, cost, and energy per agent and provider. Identify bottlenecks before users do.
Real-time performance dashboard for operators and teams. Track executions, latency, success rate, cost allocation, and sustainability trends in one place.
Failed executions surfaced with root cause classification: provider error, tool timeout, context overflow, or rate limit.
Multi-domain tenant management with dynamic theme overrides per domain. Isolate agent catalogs by business unit.
Browse practical guides, connected-app help, and product walkthroughs without leaving the portal.
Public documentation covering onboarding, product workflows, integrations, and prompting best practices.
Integrations
VDF AI connects to 21 of the systems your team already works in, so agents reason over live context instead of pasted snippets.
These resources explain the platform category, governance model, and Copilot-alternative framing behind VDF AI Agents.
How the enterprise platform layer differs from a single assistant.
AI Agent GovernanceWhy tool permissions, audit logs, and model policy matter before agents scale.
Microsoft Copilot AlternativeWhen enterprises need more control than a suite-native assistant can offer.
Everything you need to know about VDF AI Agents.
Bring one workflow and we will map it to the agents, tools, governance, and deployment model it needs — so you see what VDF AI Agents changes for your team before you commit.